pltnts <- list.files("~/R/terni/rds", pattern = "*.rds", full.names = TRUE) 

map(pltnts, \(pltnt) {
  inquinante <- tools::file_path_sans_ext(basename(pltnt))
  
  cat("\n## ", inquinante, "\n\n")

  df <- read_csv("~/R/terni/data/dataframes/df_finale.csv", show_col_types = FALSE)
  index <- grep(inquinante, names(df))
  names(df)[index] <- "value"

  rds <- readRDS(pltnt)
  mod <- getModel(names(rds), df)
  
  gamtabs(mod, type = "HTML")
  cat("\n\n")
    
  cat("R²:", summary(mod)$r.sq %>% round(3) )
cat("\n\n")
  # stargazer(mod, type="text" )

  appraise(mod) %>% print()
  draw(mod) %>% print()
  
  rm(mod)
  cat("\n\n")
})

Al_i

stringa modello: gam(log(value) ~ s(tp_max) + s(t2m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.6455 0.0048 345.0029 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(tp_max) 4.0117 4.5928 16.0655 < 0.0001
s(t2m_IQR) 1.3790 1.5696 9.1918 0.0004

R²: 0.275

Al_s

stringa modello: gam(log(value) ~ s(pbl12_min) + s(scrapyard) + s(sp_IQR) + s(wspeed_max_max) + s(tp_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 0.6054 0.0168 36.0994 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(pbl12_min) 1.0001 1.0002 18.4363 < 0.0001
s(scrapyard) 3.9608 4.8647 7.4353 < 0.0001
s(sp_IQR) 1.0000 1.0001 58.1864 < 0.0001
s(wspeed_max_max) 2.4165 2.8309 20.3789 < 0.0001
s(tp_median) 1.8030 1.9107 6.0010 0.0028

R²: 0.647

As_i

stringa modello: gam(log(value) ~ s(pbl12_median) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.7978 1099012885.8576 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(pbl12_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

As_s

stringa modello: gam(log(value) ~ s(cold_area) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -24.3739 1470767797.3404 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(cold_area) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

B_i

stringa modello: gam(log(value) ~ s(pwspeed_IQR) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -8.2656 72.9295 -0.1133 0.9098
B. smooth terms edf Ref.df F-value p-value
s(pwspeed_IQR) 4.9512 5.0933 3.7966 0.0020

R²: 0.39

B_s

stringa modello: gam(log(value) ~ s(wspeed_max) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.9984 1108.9985 -0.0216 0.9828
B. smooth terms edf Ref.df F-value p-value
s(wspeed_max) 8.0000 8.0001 6.7448 < 0.0001

R²: 0.494

Ba_i

stringa modello: gam(log(value) ~ s(pbl00_IQR) + s(imp_200) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -0.7146 234.0267 -0.0031 0.9976
B. smooth terms edf Ref.df F-value p-value
s(pbl00_IQR) 8.0001 8.0001 17.0090 < 0.0001
s(imp_200) 1.0000 1.0000 6.0656 0.0144

R²: 0.68

Ba_s

stringa modello: gam(log(value) ~ s(pbl12_mean) + s(s8_sup_200) + s(pop_200) + s(pbl12_max) + s(wspeed_IQR) + s(wspeed_max) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 0.3907 0.0170 23.0436 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(pbl12_mean) 1.0000 1.0001 0.4034 0.5259
s(s8_sup_200) 2.4015 3.0389 10.0470 < 0.0001
s(pop_200) 1.0000 1.0000 9.1488 0.0027
s(pbl12_max) 4.3058 5.0450 11.0489 < 0.0001
s(wspeed_IQR) 1.0000 1.0001 110.4326 < 0.0001
s(wspeed_max) 1.8379 2.1012 11.7025 < 0.0001

R²: 0.658

Bi_i

stringa modello: gam(log(value) ~ s(wdir_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.3786 728888923.6442 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(wdir_min) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Bi_s

stringa modello: gam(log(value) ~ s(kndvi) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.7952 820896902.7941 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(kndvi) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Ca_i

stringa modello: gam(log(value) ~ s(pblmin_max) + s(tp_IQR) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.9101 0.0047 407.1479 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(pblmin_max) 1.0001 1.0002 36.5603 < 0.0001
s(tp_IQR) 4.0549 4.5367 26.1999 < 0.0001

R²: 0.451

Ca_s

stringa modello: gam(log(value) ~ s(nirradiance_IQR) + s(bh_200) + s(s8_sup_200) + s(tmin2m_max) + s(s6_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.8921 0.0027 688.8894 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(nirradiance_IQR) 2.2636 2.6515 58.4584 < 0.0001
s(bh_200) 3.8818 4.6559 6.5691 < 0.0001
s(s8_sup_200) 5.2758 6.2124 5.8510 < 0.0001
s(tmin2m_max) 2.3224 2.8845 8.0398 < 0.0001
s(s6_sup_200) 1.0002 1.0004 8.4140 0.0040

R²: 0.529

Cd_i

stringa modello: gam(log(value) ~ s(s5_sup_200, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.4419 448071431.0465 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(s5_sup_200) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Cd_s

stringa modello: gam(log(value) ~ s(v10m_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.5796 387687954.4849 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(v10m_min) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Ce_i

stringa modello: gam(log(value) ~ s(s7_sup_200, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.4443 464327192.8477 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(s7_sup_200) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Ce_s

stringa modello: gam(log(value) ~ s(t2m_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.4449 575094374.2699 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(t2m_min) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Co_i

stringa modello: gam(log(value) ~ s(cold_area) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -69.3581 262156066.4195 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(cold_area) 1.0000 1.0000 0.0000 1.0000

R²: 0.053

Co_s

stringa modello: gam(log(value) ~ s(kndvi) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.8396 653381530.1422 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(kndvi) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Cr_i

stringa modello: gam(log(value) ~ s(cold_area) + s(wdir_IQR) + s(s6_sup_200) + s(u10m_median, k=9) + s(s1_sup_200, k=7) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.1558 0.0069 166.5089 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(cold_area) 4.6024 5.4578 61.4565 < 0.0001
s(wdir_IQR) 1.0000 1.0001 150.3107 < 0.0001
s(s6_sup_200) 7.1140 7.9155 8.7323 < 0.0001
s(u10m_median) 1.0001 1.0001 46.2379 < 0.0001
s(s1_sup_200) 1.6618 1.8638 3.8679 0.0136

R²: 0.744

Cs_i

stringa modello: gam(log(value) ~ s(wdir_max) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.1515 404332678.1999 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(wdir_max) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Cs_s

stringa modello: gam(log(value) ~ s(wspeed_min, k=8) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.6596 563533184.0972 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(wspeed_min) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Cu_i

stringa modello: gam(log(value) ~ s(pblmax_mean) + s(s5_sup_200, k=9) + s(s6_sup_200) + s(m_dis_ferr) + s(s3_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 0.7405 0.0091 81.4914 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(pblmax_mean) 4.3271 5.1926 76.1385 < 0.0001
s(s5_sup_200) 7.4404 7.7783 13.3667 < 0.0001
s(s6_sup_200) 1.1743 1.3090 5.8071 0.0080
s(m_dis_ferr) 1.0001 1.0002 26.4269 < 0.0001
s(s3_sup_200) 4.1164 4.6546 7.7620 < 0.0001

R²: 0.678

Cu_s

stringa modello: gam(log(value) ~ s(nirradiance_mean) + s(hot_area) + s(m_dis_ferr) + s(s4_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -0.2545 0.0394 -6.4615 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(nirradiance_mean) 7.9657 8.4567 26.1746 < 0.0001
s(hot_area) 8.6498 8.9592 9.3167 < 0.0001
s(m_dis_ferr) 1.0001 1.0001 10.0428 0.0017
s(s4_sup_200) 1.6659 1.9615 3.9791 0.0361

R²: 0.651

Fe_i

stringa modello: gam(log(value) ~ s(wdir_IQR) + s(cold_area) + s(wspeed_max_mean) + s(s5_sup_200, k=9) + s(s3_sup_200) + s(s4_sup_200) + s(s1_sup_200, k=7) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.8038 0.0032 558.8771 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(wdir_IQR) 3.7184 4.5237 20.0079 < 0.0001
s(cold_area) 8.9648 8.9971 26.8025 < 0.0001
s(wspeed_max_mean) 1.0000 1.0001 19.7659 < 0.0001
s(s5_sup_200) 1.0000 1.0000 33.8379 < 0.0001
s(s3_sup_200) 1.0000 1.0000 22.1570 < 0.0001
s(s4_sup_200) 1.9781 2.2997 5.6069 0.0033
s(s1_sup_200) 1.0000 1.0001 5.9050 0.0158

R²: 0.637

Fe_s

stringa modello: gam(log(value) ~ s(pbl12_mean) + s(m_dis_ferr) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 0.7988 0.0150 53.1779 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(pbl12_mean) 8.8137 8.9888 27.1550 < 0.0001
s(m_dis_ferr) 1.9068 2.3652 7.6610 0.0004

R²: 0.61

Ga_i

stringa modello: gam(log(value) ~ s(v10m_median, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.4915 399086832.8146 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(v10m_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Ga_s

stringa modello: gam(log(value) ~ s(v10m_median, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.8122 885536862.8724 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(v10m_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

K_i

stringa modello: gam(log(value) ~ s(pbl00_IQR) + s(pwspeed_min, k=7) + s(pblmin_max) + s(s6_sup_200) + s(pwspeed_IQR) + s(nirradiance_min) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.8271 0.0067 272.6002 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(pbl00_IQR) 1.5539 1.7218 21.4260 < 0.0001
s(pwspeed_min) 1.0000 1.0000 135.6251 < 0.0001
s(pblmin_max) 3.1130 3.4339 32.5927 < 0.0001
s(s6_sup_200) 1.1279 1.2438 5.2072 0.0131
s(pwspeed_IQR) 2.0066 2.2014 4.7931 0.0136
s(nirradiance_min) 1.0001 1.0002 32.4704 < 0.0001

R²: 0.727

K_s

stringa modello: gam(log(value) ~ s(sp_max) + s(sp_mean) + s(nirradiance_IQR) + s(tmax2m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.6976 0.0046 368.3042 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(sp_max) 1.0000 1.0001 354.5608 < 0.0001
s(sp_mean) 1.9732 2.4231 2.1526 0.0695
s(nirradiance_IQR) 1.0013 1.0025 12.5933 0.0004
s(tmax2m_IQR) 1.0000 1.0000 12.0815 0.0006

R²: 0.792

La_i

stringa modello: gam(log(value) ~ s(pblmin_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.4328 658563871.6587 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(pblmin_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

La_s

stringa modello: gam(log(value) ~ s(pblmin_IQR, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.9853 1033837054.2196 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(pblmin_IQR) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Li_i

stringa modello: gam(log(value) ~ s(s7_sup_200, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.1998 628699215.4837 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(s7_sup_200) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Li_s

stringa modello: gam(log(value) ~ s(pblmin_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.0640 526087096.1012 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(pblmin_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Mg_i

stringa modello: gam(log(value) ~ s(nirradiance_IQR) + s(pblmin_median, k=3) + s(sp_IQR) + s(v10m_min) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.3775 0.0049 283.4954 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(nirradiance_IQR) 2.4391 2.8824 12.5031 < 0.0001
s(pblmin_median) 1.0000 1.0000 23.8863 < 0.0001
s(sp_IQR) 1.0001 1.0001 132.3558 < 0.0001
s(v10m_min) 1.0000 1.0000 6.7063 0.0101

R²: 0.425

Mg_s

stringa modello: gam(log(value) ~ s(pbl00_max) + s(scrapyard) + s(wspeed_max_mean) + s(s3_sup_200) + s(s6_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.4034 0.0042 332.7256 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(pbl00_max) 4.8392 5.4881 22.2652 < 0.0001
s(scrapyard) 1.4159 1.7132 24.7618 < 0.0001
s(wspeed_max_mean) 2.7679 3.1948 6.2116 0.0003
s(s3_sup_200) 1.9556 2.3266 3.4066 0.0276
s(s6_sup_200) 1.0001 1.0002 6.0819 0.0143

R²: 0.509

Mn_i

stringa modello: gam(log(value) ~ s(s3_sup_200) + s(wdir_IQR) + s(nirradiance_max) + s(cold_area) + s(pblmin_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 0.7850 0.0082 95.3319 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(s3_sup_200) 7.6200 7.7565 19.5203 < 0.0001
s(wdir_IQR) 1.0000 1.0000 60.7915 < 0.0001
s(nirradiance_max) 1.0000 1.0000 29.9658 < 0.0001
s(cold_area) 7.4337 8.1583 5.4911 < 0.0001
s(pblmin_median) 1.0000 1.0000 5.7412 0.0173

R²: 0.653

Mn_s

stringa modello: gam(log(value) ~ s(pbl12_mean) + s(scrapyard) + s(nirradiance_IQR) + s(wspeed_max) + s(s7_sup_200, k=3) + s(s6_sup_200) + s(cold_area) + s(pwspeed_max) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 0.5601 0.0114 49.0079 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(pbl12_mean) 1.0000 1.0001 100.1215 < 0.0001
s(scrapyard) 1.0000 1.0000 44.9631 < 0.0001
s(nirradiance_IQR) 2.3936 2.7712 67.2165 < 0.0001
s(wspeed_max) 0.9928 1.2275 1.8230 0.2093
s(s7_sup_200) 1.0000 1.0000 6.0225 0.0148
s(s6_sup_200) 1.1998 1.3712 9.0031 0.0031
s(cold_area) 1.0000 1.0000 13.4237 0.0003
s(pwspeed_max) 1.9928 2.2275 2.2804 0.1033

R²: 0.705

Mo_i

stringa modello: gam(log(value) ~ s(pblmax_min) + s(cold_area) + s(s1_sup_200, k=7) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -0.2418 0.0974 -2.4819 0.0137
B. smooth terms edf Ref.df F-value p-value
s(pblmax_min) 7.8757 8.3728 22.1125 < 0.0001
s(cold_area) 2.8465 3.5748 26.8501 < 0.0001
s(s1_sup_200) 1.7710 1.9837 5.6332 0.0028

R²: 0.668

Mo_s

stringa modello: gam(log(value) ~ s(cold_area) + s(nirradiance_mean) + s(s3_sup_200) + s(pblmin_median, k=3) + s(m_dis_ferr) + s(pbl00_mean) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 0.4577 0.0168 27.2783 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(cold_area) 3.5956 4.3740 34.6549 < 0.0001
s(nirradiance_mean) 2.6442 3.2531 33.3174 < 0.0001
s(s3_sup_200) 7.5440 7.8310 14.9215 < 0.0001
s(pblmin_median) 1.0000 1.0000 27.9580 < 0.0001
s(m_dis_ferr) 1.0001 1.0002 11.5412 0.0008
s(pbl00_mean) 1.0000 1.0000 9.5925 0.0022

R²: 0.755

Na_i

stringa modello: gam(log(value) ~ s(pwspeed_min, k=7) + s(v10m_mean) + s(tmax2m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.7114 0.0048 360.1878 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(pwspeed_min) 1.0000 1.0001 61.0800 < 0.0001
s(v10m_mean) 1.0000 1.0000 18.3960 < 0.0001
s(tmax2m_IQR) 1.0000 1.0000 8.6452 0.0036

R²: 0.203

Na_s

stringa modello: gam(log(value) ~ s(u10m_IQR) + s(s7_sup_200, k=3) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.7578 0.0035 505.0096 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(u10m_IQR) 8.0000 8.0000 39.9308 < 0.0001
s(s7_sup_200) 1.8868 1.9872 5.6379 0.0044

R²: 0.562

Nb_i

stringa modello: gam(log(value) ~ s(rh_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.4201 969820731.2887 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(rh_min) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Nb_s

stringa modello: gam(log(value) ~ s(v10m_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.6778 916551596.4583 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(v10m_min) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Ni_i

stringa modello: gam(log(value) ~ s(cold_area) + s(wdir_IQR) + s(s6_sup_200) + s(s1_sup_200, k=7) + s(wdir_median) + s(t2m_IQR) + s(sp_mean) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 0.8086 0.0120 67.2528 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(cold_area) 4.7186 5.5927 57.8875 < 0.0001
s(wdir_IQR) 1.0000 1.0000 47.3159 < 0.0001
s(s6_sup_200) 6.9075 7.7226 6.2336 < 0.0001
s(s1_sup_200) 1.3748 1.5519 4.1753 0.0135
s(wdir_median) 1.0000 1.0000 29.2577 < 0.0001
s(t2m_IQR) 1.0000 1.0000 10.7289 0.0012
s(sp_mean) 1.0000 1.0000 7.1496 0.0080

R²: 0.722

Ni_s

stringa modello: gam(log(value) ~ s(m_dis_ferr) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -2171.6198 822.9713 -2.6388 0.0088
B. smooth terms edf Ref.df F-value p-value
s(m_dis_ferr) 1.0000 1.0000 6.9647 0.0088

R²: 0.231

Pb_i

stringa modello: gam(log(value) ~ s(nirradiance_mean) + s(s3_sup_200) + s(wspeed_IQR) + s(wspeed_max_max) + s(wspeed_max_min) + s(wdir_IQR) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 0.3775 0.0140 26.9697 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(nirradiance_mean) 1.0000 1.0000 32.1711 < 0.0001
s(s3_sup_200) 7.4182 7.8474 28.0831 < 0.0001
s(wspeed_IQR) 1.0000 1.0000 0.2321 0.6304
s(wspeed_max_max) 2.3873 2.8503 6.3126 0.0006
s(wspeed_max_min) 1.0000 1.0000 12.3599 0.0005
s(wdir_IQR) 1.0000 1.0000 6.7340 0.0100

R²: 0.673

Pb_s

stringa modello: gam(log(value) ~ s(sp_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.7611 2017.3248 -0.0113 0.9910
B. smooth terms edf Ref.df F-value p-value
s(sp_min) 1.0000 1.0001 0.0005 0.9891

R²: 0.237

PM10

stringa modello: gam(log(value) ~ s(v10m_min) + s(pwspeed_IQR) + s(v10m_median, k=9) + s(pop_200) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.2172 0.0039 314.5843 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(v10m_min) 2.0134 2.3286 21.4008 < 0.0001
s(pwspeed_IQR) 3.9684 4.4831 68.1847 < 0.0001
s(v10m_median) 1.0001 1.0002 24.7028 < 0.0001
s(pop_200) 1.0001 1.0001 6.1866 0.0135

R²: 0.754

Rb_i

stringa modello: gam(log(value) ~ s(pblmin_IQR, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.8260 713180328.4144 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(pblmin_IQR) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Rb_s

stringa modello: gam(log(value) ~ s(tmax2m_mean) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -9.6249 9.3176 -1.0330 0.3025
B. smooth terms edf Ref.df F-value p-value
s(tmax2m_mean) 1.2578 1.4561 0.8830 0.2305

R²: 0.323

Sb_i

stringa modello: gam(log(value) ~ s(rh_max) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -131.5374 349025.3924 -0.0004 0.9997
B. smooth terms edf Ref.df F-value p-value
s(rh_max) 1.0001 1.0002 0.0001 0.9955

R²: 0.124

Sb_s

stringa modello: gam(log(value) ~ s(tmin2m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -24.6092 1429869567.4169 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(tmin2m_IQR) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Sn_i

stringa modello: gam(log(value) ~ s(pbl12_mean) + s(hot_area) + s(s4_sup_200) + s(s8_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -0.3424 0.0494 -6.9266 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(pbl12_mean) 1.0000 1.0000 332.2339 < 0.0001
s(hot_area) 2.1122 2.6346 19.1946 < 0.0001
s(s4_sup_200) 1.0001 1.0001 7.2134 0.0077
s(s8_sup_200) 1.6335 2.0390 5.9573 0.0029

R²: 0.779

Sn_s

stringa modello: gam(log(value) ~ s(pblmin_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.0719 467030630.5307 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(pblmin_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Sr_i

stringa modello: gam(log(value) ~ s(tp_IQR) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -2.9084 5.5494 -0.5241 0.6006
B. smooth terms edf Ref.df F-value p-value
s(tp_IQR) 7.0532 7.1164 3.4669 0.0007

R²: 0.441

Sr_s

stringa modello: gam(log(value) ~ s(v10m_median, k=9) + s(imp_200) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -1.6333 102.5148 -0.0159 0.9873
B. smooth terms edf Ref.df F-value p-value
s(v10m_median) 7.0000 7.0001 23.6825 < 0.0001
s(imp_200) 1.0000 1.0000 6.9530 0.0089

R²: 0.61

Ti_i

stringa modello: gam(log(value) ~ s(rh_max) + s(pop_200) + s(pblmin_IQR, k=9) + s(s3_sup_200) + s(s4_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 0.4315 0.0140 30.7584 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(rh_max) 3.1428 3.8193 39.0249 < 0.0001
s(pop_200) 6.4832 7.1177 8.2150 < 0.0001
s(pblmin_IQR) 1.0001 1.0001 42.6792 < 0.0001
s(s3_sup_200) 3.8167 4.1252 4.0033 0.0017
s(s4_sup_200) 1.0001 1.0001 17.1647 < 0.0001

R²: 0.528

Ti_s

stringa modello: gam(log(value) ~ s(u10m_mean) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -23.7015 1110537246.7412 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(u10m_mean) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Tl_i

stringa modello: gam(log(value) ~ s(s5_sup_200, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.8890 910243221.4849 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(s5_sup_200) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Tl_s

stringa modello: gam(log(value) ~ s(s3_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.5587 419614258.4032 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(s3_sup_200) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

U_i

stringa modello: gam(log(value) ~ s(kndvi) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.2572 573815329.4478 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(kndvi) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

U_s

stringa modello: gam(log(value) ~ s(tp_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.2573 670082786.1382 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(tp_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

V_i

stringa modello: gam(log(value) ~ s(tmin2m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -24.8338 1374423269.9621 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(tmin2m_IQR) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

V_s

stringa modello: gam(log(value) ~ s(t2m_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
## Warning in newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L,
## : Adattamento terminato con errore di passo: controllare attentamente i
## risultati
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -42.5595 68.3869 -0.6223 0.5342
B. smooth terms edf Ref.df F-value p-value
s(t2m_min) 1.0000 1.0000 0.3700 0.5435

R²: 0.131

W_i

stringa modello: gam(log(value) ~ s(u10m_median, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.5540 418208955.1281 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(u10m_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

W_s

stringa modello: gam(log(value) ~ s(u10m_median, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.7686 503187178.7599 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(u10m_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Zn_i

stringa modello: gam(log(value) ~ s(wdir_mean) + s(nirradiance_max) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.2598 0.0071 176.2748 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(wdir_mean) 6.8044 7.6989 22.9497 < 0.0001
s(nirradiance_max) 1.0001 1.0002 3.0862 0.0801

R²: 0.41

Zn_s

stringa modello: gam(log(value) ~ s(nirradiance_mean) + s(scrapyard) + s(v10m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) 1.1164 0.0075 148.6038 < 0.0001
B. smooth terms edf Ref.df F-value p-value
s(nirradiance_mean) 4.9428 5.4388 51.9507 < 0.0001
s(scrapyard) 3.2254 4.0040 9.9808 < 0.0001
s(v10m_IQR) 3.0812 3.6322 26.1013 < 0.0001

R²: 0.754

Zr_i

stringa modello: gam(log(value) ~ s(sp_median) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -24.5748 935817661.1736 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(sp_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

Zr_s

stringa modello: gam(log(value) ~ s(pbl00_median, k=6) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN
A. parametric coefficients Estimate Std. Error t-value p-value
(Intercept) -22.7011 677221618.5782 -0.0000 1.0000
B. smooth terms edf Ref.df F-value p-value
s(pbl00_median) 1.0000 1.0000 0.0000 1.0000

R²: -0.004

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